Systems and methods for user detection, identification, and localization within a defined space

ABSTRACT

A system and method includes detecting motion in a space; capturing images of a scene in a direction of the motion within the space by peripheral image capturing devices and detecting a body of a person within the images of the scene; extracting an image of the body from the images of the scene; identifying a head of the body based on the extracted at least one image of the body and extracting an image of a head from the extracted the one image of the body; transmitting the one image of the body to a body tracking module; transmitting the extracted image of the head to a face detection module; in parallel, tracking the body within the space and performing by a facial recognition module facial recognition of the extracted image of the head of the body; and determining an identity of a user associated with the body.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/509,624 filed 22 May 2017, which is incorporated in its entirety bythis reference.

TECHNICAL FIELD

The inventions of the present application relate generally to theassistive automation technologies, and more specifically to new anduseful computer vision systems and techniques for processing visual databy an assistive automation device.

BACKGROUND

Many advances are being made in assistive automation technologies. Manyof these advances involve artificially intelligent interactive deviceswhich enable a user to communicate with the interactive devices toprovide instructions for automating several home processes and/orotherwise, for placing general inquiries and the like. While theseexisting home automation systems and automated virtual assistant systemstypically include an interface allowing a user to audibly and/orphysically interact with the system, these existing systems lack robustimaging components that allow for an efficient and accurateidentification and tracking of a user within a space and additionally,localization of the user in the space.

Thus, there is a need in the assistive automation device field to createnew and useful systems and methods for visually interfacing with a userby implementing novel computer vision techniques. The embodiments of thepresent application provide such new and useful systems and methods.

BRIEF SUMMARY OF THE INVENTION(S)

Thus, there is a need in the assistive automation device field to createnew and useful systems and methods for visually interfacing with a userby implementing novel computer vision techniques. The embodiments of thepresent application provide such new and useful systems and methods.

Embodiments of the present application include systems and method forintelligent user detection, localization, and/or identification thatincludes a plurality of peripheral image capturing devices arrangedabout a body of the system, wherein the plurality of peripheral imagecapturing device capture one or more images of a scene within a space; adatabase storing biometric image data of one or more users; a bodyextraction module that extracts at least one image of the body from theone or more images of the scene, wherein the body extraction modulefurther extracts an image of a head from the extracted at least oneimage of the body; a body tracking module that tracks the body withinthe space based on the extracted at least one image of the body; afacial recognition module that performs facial recognition of theextracted image of the head of the body in parallel with the bodytracking module; a processing circuit that: determines an identity of auser associated with the body based on results of the facial recognitionmodule; in response to determining the identity of the user, augmentsthe image frames of the captured one or more images that include thebody with identity data based on the determined identity of the user.

In one embodiment, the systems and methods include one or more movementdetection sensors that detect motion and trigger an operation of theplurality of peripheral image capturing devices.

In one embodiment, the systems and methods include a high-resolutioncamera that captures images of the scene at a higher image resolutionthan the plurality of peripheral cameras, wherein the high-resolutioncamera is triggered to operate when an image quality of the one or moreimages captured by the plurality of peripheral cameras is below a facialrecognition threshold.

In one embodiment, tracking the body includes: augmenting each of theimage frames of the captured one or more images that include the bodywith tracking data, wherein the tracking data comprises coordinate dataof a position of the body within the space; and predicting a movement ofthe body within the space based on the image frames having trackingdata.

Further embodiments of the present application provide systems andmethods that include automatically tracking a user within a space anddetermining an identity of the user based on image data that includesautomatically detecting, by a motion sensor, motion in a space; inresponse to detecting the motion in the space, capturing one or moreimages of a scene in a direction of the motion within the space by oneor more of a plurality of peripheral image capturing devices anddetecting a body of a person within at least one of the one or moreimages of the scene; extracting at least one image of the body from theone or more images of the scene; identifying a head of the body based onthe extracted at least one image of the body and extracting an image ofa head from the extracted at least one image of the body; transmittingthe extracted at least one image of the body to a body tracking module;transmitting the extracted image of the head to a face detection module;in parallel, (i) tracking the body within the space based on theextracted at least one image of the body and (ii) performing by a facialrecognition module facial recognition of the extracted image of the headof the body based on the extracted image of the head; determining anidentity of a user associated with the body based on results of thefacial recognition module; and in response to determining the identityof the user, augmenting image frames of the captured one or more imagesthat include the body within the scene with identity data based on thedetermined identity of the user.

In one embodiment, the systems and methods include identifying an imageresolution of the extracted image of the head of the body; determiningthat the image resolution of the extracted image of the head of the bodydoes not satisfy a minimal facial recognition threshold; in response todetermining that the image resolution does not satisfy the minimalfacial recognition threshold, providing an activation signal to ahigh-resolution image capturing device distinct from the plurality ofperipheral cameras; and capturing by the high-resolution image capturingdevice one or more high-resolution images of a scene within the space.

In one embodiment, the minimal facial recognition threshold comprises aminimal image resolution required to successfully perform facialrecognition of a user based on image data of a head, and thehigh-resolution image capturing device comprises an image capturingdevice that captures images that satisfy the minimal facial recognitionthreshold.

In one embodiment, the systems and methods include identifying the bodyof the person within the one or more high-resolution images of thescene; extracting at least one high-resolution image of the body fromthe one or more high-resolution images; extracting a high-resolutionimage of a head of the body from the at least one high-resolution imageof the body; transmitting to the facial recognition module the extractedhigh-resolution image of the head of the body, wherein determining theidentity of the user associated with the body is based on results of thefacial recognition using the extracted high-resolution image of the headof the body.

In one embodiment, the systems and methods include augmenting each ofthe image frames of the captured one or more images that include thebody with tracking data, wherein the tracking data comprises coordinatedata of a position of the body within the space; and predicting amovement of the body within the space based on the image frames havingtracking data.

In one embodiment, the prediction of the movement of the body comprisesa predicted trajectory that identifies a plurality of future coordinatelocations of the body within the space.

In one embodiment, the systems and methods include selectivelyactivating a subset of the plurality of peripheral image capturingdevices having a field-of-view in one or more areas within the spacebased on the predicted trajectory of the body.

In one embodiment, the systems and methods include augmenting trackingdata, by the body tracking module, to each of the image frames thatincludes an image of the body, wherein the tracking data comprisescoordinate data comprising X-axis coordinate data and Y-axis coordinatedata of the body based on an X-Y coordinate system defined for thespace, wherein the X-Y coordinate system is based on a position of theplurality of the peripheral image capturing devices functioning as apoint of origin of the X-Y coordinate system in the space.

In one embodiment, the systems and methods include providing the one ormore captured images as input into an object detection module; using theobject detection module to detect one or more objects having an image ofa body and/or an image of a face included in the one or more objects;flagging the one or more detected objects as objects that include datacapable of triggering a false positive identification of a body or auser; and providing data relating to the one or more detected objects asinput into a body detection module.

In one embodiment, the high-resolution image capturing device comprisesa movable image capturing device that is movable relative to stationarypositions of the plurality of peripheral image capturing devices, andthe high-resolution image capturing device includes a greater focallength than a focal length of each of the plurality of peripheralcameras.

In one embodiment, the systems and methods include calculating a speedof movement of a body in a plurality of images of the body captured bythe plurality of peripheral cameras, wherein if the speed of movement ofthe body does not satisfy a movement threshold, providing actualcoordinate data of the body as input for generating movementinstructions for the high-resolution image capturing device; andgenerating movement instructions for the high-resolution image capturingdevice based on the actual coordinate data of the body.

In one embodiment, the systems and methods include calculating a speedof movement of a body in a plurality of images of the body captured bythe plurality of peripheral cameras, wherein if the speed of movement ofthe body satisfies a movement threshold, providing predicted coordinatedata of the body as input for generating movement instructions for thehigh-resolution image capturing device; and generating movementinstructions for the high-resolution image capturing device based on theactual coordinate data of the body.

In one embodiment, the systems and methods include augmenting the one ormore images with an ephemeral identifier for the body recognized in theone or more images; and in response to determining the identity of theuser associated with the body, replacing the ephemeral identifier withthe identity of the user.

In one embodiment, the systems and methods include providing the one ormore images of the scene as input into a gait recognition module; usingthe gait recognition module to determine the identity of the user basedon gait data derived from the one or more images; and using the gaitdata to validate the identity of the user generated by the facialrecognition module.

In one embodiment, the systems and methods include providing the one ormore images of the scene as input into a body joint recognition module;using the body joint recognition module to determine the identity of theuser based on body joint data derived from the one or more images; andusing the body joint data to validate the identity of the user generatedby the facial recognition module.

Embodiments of the present application include a method and a system forautomatically detecting, by a presence sensor, a presence of a humanbody in a space; in response to detecting the presence of the human bodyin the space, capturing one or more images of a scene in a direction ofthe presence within the space by one or more of a plurality ofperipheral image capturing devices and detecting a body of a personwithin at least one of the one or more images of the scene; extractingat least one image of the body from the one or more images of the scene;identifying a head of the body based on the extracted at least one imageof the body and extracting an image of a head from the extracted atleast one image of the body; transmitting a first image file comprisingthe extracted at least one image of the body to a body tracking module;transmitting a second image file comprising the extracted image of thehead to a face detection module; in parallel, (i) tracking the bodywithin the space based on the extracted at least one image of the bodyand (ii) performing by a biometric recognition module biometricrecognition of the extracted image of the head of the body based on theextracted image of the head; determining an identity of a userassociated with the body based on results of the biometric recognitionmodule; and in response, augmenting image frames of the captured one ormore images that include the body within the scene with identity databased on the determined identity of the user.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a system in accordance with one or more embodimentsof the present application;

FIG. 2 illustrates a method in accordance with one or more embodimentsof the present application;

FIG. 3 illustrates a schematic demonstrating body and face detection inaccordance with one or more embodiments of the present application; and

FIG. 4 illustrates another schematic demonstrating body tracking inaccordance with one or more embodiments of the present application.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the presentapplication are not intended to limit the inventions to these preferredembodiments, but rather to enable any person skilled in the art of tomake and use these inventions.

1. System for Automatically Identifying a User

As shown in FIG. 1, the system 100 includes a device 110 having aplurality of peripheral cameras (image capturing devices) 120, apan/tilt and zoom (PTZ) camera 130, one or more movement detectionsensors 140, a database 150, and controller 160.

The system 100 functions to implement a novel computer technique todetect a user in a space and further identify the user. In particular,using the one or more movement detection sensors 140, the system 100functions to detect when a user has entered a space or generally, whenany motion has occurred in a space surrounding the system 100. Anymovement detected by the one or more movement detection sensors 140would serve as an event trigger that causes an operation of theplurality of peripheral cameras 120 of the system 100. In someembodiments, when system 100 is idle, only the one or more movementdetectors 140 may be operable to detect motion while the remainingcomponents of the system 100 remain dormant. Because the system 100, insome embodiments, may be operable via a constrained device or the like(e.g., limited memory, limited computing power, and limited energysupply, etc.), it provides additional benefits to conserve deviceresources when there is no or limited activity to detect by the system100. It shall be noted that in an idle state of the system 100 any ofthe one or more energy-consuming components or resources may be madeidle or maintained in an active state.

When the plurality of peripheral cameras 120 are triggered to operateand capture image data, the system 100 functions to capture images ofone or more bodies in the viewable areas of at least some of theperipheral cameras 130. The image data captured by the plurality ofperipheral cameras 120 may then be used as input into several imageprocessing modules of the system 100. Each of the processing modulesdescribed herein may be implemented by computer processing circuitry,computer processor unit, microcomputer, microcontroller, or the like.Additionally, or alternatively, some or all of the processes of theprocessing modules of the system 100 may be implemented via adistributed network of servers or computers (e.g., cloud computing).

The processing modules may implement one or more body detection, facedetection, body tracking, and various predictive algorithms. In someinstances, the process detection algorithms seek to identify a body anda face from the image data. In the circumstance that the processingimages are not capable or confidently capable of identifying a face inthe image data, this may trigger an operating signal or command to thePT camera 130 in order to capture a face in an image scene that issuitable for facial recognition processing.

Thus, the system 100 functions to accurately provide facial images thatcan be used to accurately and efficiently identify a user andcontemporaneously or simultaneously, provide body image data to trackmovements of a user. Once a user's identity is determined at the facialrecognition processes, the user identity information may be added to thebody tracking information to inform the system regarding an identity ofthe body being tracked. Once the user ID and body information are inassociation, the system 100 may be more capable of meaningfullyinteracting with the user based on specific patterns and preferences ofthe user.

The plurality of peripheral cameras 120 may include a plurality of RGBcameras that are circumferentially positioned (either interior surfaceor exterior surface) about the system 100. In some embodiments, theplurality of peripheral cameras 120 are fixed in their arrangement withrespect to the system 100. The system 100 may employ any type of imagesensor (e.g., scanners, and the like), image capture system, or the likethat is capable of acquiring image data from circumstances surroundingthe system 100.

The PT camera 130 may include a camera that is moveable about multipleaxes (e.g., X, Y) and able to zoom along a Z-axis or the like. The PTcamera 130 may be used in combination with the plurality of peripheralcameras 120 to identity one or more features of a body that is detectedby the system 100. Relative to the peripheral cameras 120, the PT camera130 may have a greater focal length allowing the PT camera to captureimages that would typically outside of an accurate imaging range of theplurality of peripheral cameras 120. In one example, the PT camera 130may be used to capture images of a face of a body where the plurality ofperipheral cameras could not accurately provide such image. Accordingly,the PT camera 130 may be an image capturing device capable of capturingimage data with a higher image resolution that is greater than a lowerimage resolution of the plurality of peripheral cameras 120.

In another example, the plurality of peripheral cameras 120 or anothersensor (e.g., a depth sensor) may detect that a body image or the likeis outside of the facial recognition zone (e.g., beyond a facialrecognition threshold based on a radius or distance from the system 100or the like) of the plurality of peripheral cameras 120. In this regard,when such detection is made, the system 100 may automatically generate asignal to trigger an operation of the PT camera 130 to begin capturingbody images. Thus, in such embodiments, the triggering of the PT camera130 may be based on distance thresholds and upon detecting that anobservable object or body exceeds that threshold, this may cause theoperation of the PT camera 130 to actuate a zoom function or anincreased resolution function to capture better quality image data thanwould otherwise be obtainable by the plurality of peripheral cameras120.

The database 150 may be a database storing image data of known usersand/or known objects. The image data may include facial images, bodyimages (partial or full), facial feature images (or data), object images(e.g., common objects or standard objects, etc.) and any suitable datathat may be used to identify a known user and/or identify objects in animage scene. Thus, the database 150 may also include voice data andother biometric data of known users. Additionally, the database 150 maybe implemented locally on the system 100 to improve security of theimage data and user data captured and/or stored within database 150.However, in some embodiments, it is possible to offload some of thestorage requirements of the system 100 to a remote storage system (e.g.,the cloud).

The controller 160 preferably includes a microcontroller that includes aprocessing circuit (e.g., a CPU, a GPU, an ASIC, or any suitableintegrated circuit), memory, one or more communication interfaces, aclock generator, and peripherals, such as timers, counters, PWMgenerators. The controller 160 functions to control each of theplurality of peripheral cameras 120, the PT camera 130, as well as theprocessing modules for processing image data captured by the system 100according to one or more of the methods and/or processes describedherein.

2. Method for Automatically Identifying a User

As shown in FIG. 2, a method 200 includes automatically detecting motionin a defined space S210, capturing with peripheral cameras one or moreimages of a scene and detecting a body of a person in the scene S220,cropping an image of the detected body S230, transmitting the croppedimage of the body to a body tracking module and to a face detectionmodule S240, extracting or cropping an image of a head (including aface) of the body S250, initiating body tracking and augmentation ofbody tracking data to body image S260, identifying an identity of a userusing the face of the body S270, and updating the tracking data with theidentity of the user S280. Optionally, the method 200 includespredicting a movement of the body within the scene S265, andautomatically triggering a second moveable camera to capture an image ofa face of the body S255.

The method 200 preferably functions to implement a user detection,localization, and identification using at least a first camera or set ofcameras and preferably, using at least a second camera that is operatedin one or more circumstances or in predetermined circumstances. Inparticular, the method 200 employs a first set of cameras (imagecapturing devices) to identify, at least, a body of a user andpotentially, a face of the user in a set of captured images. The secondcamera may be activated in the circumstance that the first camera is notable to accurately capture a face of the user for facial recognitionprocessing or alternatively, a request is made to a system implementingthe method 200 to capture an image of a user with greater accuracy thata previously captured image. That is, in the case, that the imageresolution of the captured images of the body of the person or user doesnot include images having sufficient resolution to detect a face of theuser and/or determine an identity of the user, then an activation signalmay be transmitted to a second camera that is distinct from the firstcamera or first set of cameras for capturing an image of the body in animage resolution that is higher than an image resolution of the firstcamera or first set of cameras.

In some embodiments, when provided the set of captured images, themethod 200 functions to track the body through all image frames and mayadditionally, and/or alternatively, predict a movement of the body or atrajectory of the body within the space. Preferably, the method 200functions to provide a facial recognition for the associated body inparallel with the body tracking. Resultantly, method 200 is able toassign tracking data to the body and also, a user identity once facialrecognition processing is successfully completed. Contemporaneously, themethod 200 may function to localize the body within a space. The method200 may additionally function to augment localization data to imageframes that include the tracked and/or identified body.

S210, which includes automatically detecting motion in a defined space,functions to implement one or more sensors capable of detecting motionor a presence (e.g., presence detectors) of a body within a definedspace. Accordingly, in some alternative implementations, the presencedetectors may include thermal detectors, acoustic detectors, LIDAR, orany suitable short-range or long-range detectors and the like that mayfunction to detect the presence of a user. The defined space may bedefined based on one or more viewable areas of one or more cameras (orany type of suitable sensors). Accordingly, the defined space may bedetermined according to a field-of-view of the first set of cameras(e.g., the plurality of peripheral cameras) and/or the second camera(e.g., the high-resolution camera). The defined space may be anysuitable space in which one or more users or bodies of users maytypically be detected by one or more components of a system implementingthe method 200 or any other suitable processes described herein.Accordingly, the defined space may be any area including small spaces orlarge spaces including structured rooms, unstructured spaces (e.g.,spaces without walls), and the like. For instance, the system 100 may beimplemented in a small space, such as a room (e.g., a living room) or alarge space, such as an airport.

The one or more sensors implemented by S210 may preferably be microwavedetectors. The microwave detectors may function to transmit waves withinthe defined space and based on a movement of any object or user withinthe defined space, the microwave detection is configured to receive areflection amount of the waves and process a detection. It shall benoted that the one or more sensors may be any type of sensors including,but not limited to, optical, acoustic, passive infrared, ultrasonic,video cameras, gesture detectors, tomographic motion detector, as wellas varying types of microwave detectors (e.g., radars and the like).

S210, upon detecting motion in the defined space, also functions to senda signal to a plurality of peripheral cameras. The signal to theplurality of peripheral cameras may indicate motion and thus, the signalfunctions initiate or trigger an operation of the peripheral cameras.Additionally, or alternatively, upon detection of motion in the space,S210 may also initiate or trigger the operation of a plurality processesincluding body tracking and movement prediction, as well as facialrecognition processes.

Preferably, the signal to the plurality of peripheral cameras includes adirection of the motion and/or a zone with the defined space in whichthe motion was detected. For instance, the one or more sensors maydetect motion in northern section of a room, but not in a southernsection of the room. In such case, the motion detector may provide asignal indicating that motion was detected and also, data thatidentifies that the motion was detected in the northern section of theroom. S210 may function to use the motion data including the motiondirection or motion zone (section) or the like to selectively activate asubset of the plurality of peripheral cameras. In a preferredembodiment, the plurality of peripheral cameras may be arranged about acentral body of a system implementing the method 200. In such case, notall of the peripheral cameras are required to capture images when only aportion of the defined space includes activity of a user or the like.Accordingly, S210 may selectively activate only the subset of peripheralcameras that has a field-of-view of the activity based on the motiondirection or section data. S210 may additionally, and/or alternatively,activate within the subset one or more peripheral cameras into whichmotion of a body of the user is predicted to enter a field-of-view. Thatis, while the motion may be detected in a first area or section, themethod 200 may predict motion to another area or section andcorrespondingly, selectively activate, in advance, one or moreperipheral cameras with fields-of-view within the predicted areas ofmotion.

Additionally, S210 may function to use the motion data to verify in oneor more outputs of the recognition modules and body tracking modulesdescribed herein (including a movement prediction module). Accordingly,the motion data may be used to validate (or invalidate) or confirmoutputs of one or more modules described herein including validating apresence of human body, verifying a (predicted or estimated) directionof motion of the body, verifying a position (e.g., sitting, standing,lying down) of a body with respect to one or more objects within a spaceor the like.

S220, which includes capturing with one or more of the plurality ofperipheral cameras one or more images of a scene and detecting a body ofa user in the images of the scene, functions to implement an operationof at least one of a plurality of peripheral cameras for capturingimages of a scene indicated as having motion by the motion or presencedetectors in S210. Additionally, or alternatively, S220 may function tocontinuously implement the plurality of peripheral cameras even absent aspecific motion or presence signals from a sensor source (e.g., sensorsof S210). Depending on a location of the detected motion, only aselected number of the plurality of peripheral cameras may be activated,in some embodiments. In particular, it may be preferable that a subsetof cameras having a viewable range within the detected motion area areactivated to capture images. In this way, a power consumption of asystem implemented method 200 may be reduced.

As described in some detail above, in one variation, once a motiondetection area is identified by the motion sensors, the additionalperipheral cameras that do not have a viewable area in the motiondetection area may only be activated to capture images based on aprediction of a movement of an object or body within scenes captured bythe activated peripheral cameras. Thus, ahead of a user entering aspace, the method 200 may function to predict that the user will enterthat space and based on this prediction, selective peripheral cameraswith a viewable area in that space that the user will be entering may beactivated in advance of the user actually entering that space. Ofcourse, if motion is detected at another area in the defined space bythe motion detectors, this additional detection would function toactivate any peripheral cameras having a viewable area that includes theareas in which new motion is detected.

Additionally, S220 may additionally function to transmit the capturedimages of the scene to a body detection module preferably implementing abody detection algorithm. The body detection module receiving thecaptured images may function to detect whether a body of a person or thelike exists in some or all of the plurality of captured images. The bodydetection algorithm may include a machine learning model with human bodyedge detection capabilities or any suitable body detection process oralgorithm for identifying one or more bodies within a scene. It shall benoted that an object detection module implementing an object detectionalgorithm may similarly be implemented, which may, in turn, assist bodydetection by detecting non-body objects and correspondingly, enablingthe method 200 to eliminate false positives of a body within a scene.For instance, in some embodiments, objects within a defined space mayinclude a body of a user and/or a face of a user. These false positiveobjects may include televisions or displays, pictures or picture frames,mirrors or any other suitable object that may include a body and/or aface of a user (i.e., false positive data). In such instances, theobject detection module may be useful in identifying an object withpotential false positives and flag or otherwise, provide the object datato the body detection modules (as well as the facial recognition module)to avoid a detection of a body of a user within those objects therebyavoiding false positives. By providing the object data to the bodydetection module, these objects in an image may be dropped from theimage of the scene, specifically marked within the image of the scene,or the like to indicate to the body detection module that the detectedobject may include data that may trigger a false positive detection of abody and/or a user.

S220 implementing the body detection module may be capable of detectinga body image even based on a partial body image or a full body imagewithin a scene. For instance, if in a captured scene only includes abottom portion of a body from a belly position of the body down towardsfeet of the body are viewable, the body detection module may stilldetect a body from this partial body data within the scene. The bodydetection module may function to detect any suitable body components(e.g., an arm, a leg, a torso, a hand, a foot, only a head, part of ahead, etc.) for identifying a partial body image within a scene.

S220 further includes identifying image frames having a potential bodywithin a scene of the image frames and transmitting the image frames tobody extraction module. The body extraction module, as discussed in thedescription below, may implement one or more image processing techniquesto identify and specifically, extract a body from each of the imageframes captured from a scene. In some embodiments, the body detectionmodule and the body extraction module may be a single or combined modulethat performs body detection in an image scene and body extraction ofthe detected body.

Additionally, in identifying the image frames having a potential body,S220 includes marking or flagging the potential body. The marking orflagging can simply point out a location or provide coordinate locationdata within an image frame where the body detection module hasidentified as having a body. Preferably, the body detection module marksor flags the body by providing an outlining indicator that surrounds asilhouette of the potential body in the image frame. S220 may generate asilhouette that may be granular, such that the silhouette preciselyoutlines or follows a shape a detected body within an image scene.Alternatively, S220 may generate a silhouette that may be course (e.g.,a box), such that the silhouette broadly captures the body includingareas immediately outside of a shape of the detected body. Additionally,or alternatively, precise coordinate information (e.g., localizationdata) of the body location within each image frame may be generated andprovided as input. The coordinate information may be of the entire bodyand/or of a periphery (or silhouette) of the body. In this way,augmenting the detected body data with coordinate data of the detectedbody improves a probability of extracting a body at the body extractionmodule because less analysis of the image frames will be required todetermine a body location. That is, portions of an image frame that doesnot include a marking or flagging of the potential body and/or acoordinate of the potential body will be dropped from analysis, suchthat only a limited section of the image frame is considered by theprocessing modules (e.g., body detection modules, body tracking modules,facial recognition modules, etc.).

It shall be noted that while S220 generally functions to collect imagedata that may be used to identify a silhouette (edges) or a shape of ahuman body, S220 in some variations may additionally function to captureother biometric data of a user including joint data, gait data, voicedata, and the like. Joint data preferably includes positions and/orlocations of each of a user's joints along their body. Thus, an analysisof the one or more image frames captured may also result in anidentification of the joints of a body of a user. Consequently, jointdata for a detected body may be generated and also, used as input toinform an identity of the user associated with the body. Gait datapreferably includes data relating to a person's manner of walking. S220preferably includes an analysis of a detected body across multipleimages frames that enables a determination of a gait associated with thebody. Similar to joint data, gait data for a detected body may begenerated and used as input to inform an identity of the user associatedwith the body.

S230, which includes cropping an image of the detected body, functionsto receive as input into a body extraction module each of the imageframes identified in S220 as having an image of a body (or partialbody). S230 analyzes each of the image frames to determine anextractable body or body features and further to determine non-bodyfeatures within each of the images frames. The non-body features in theimage or scene may include objects and circumstances surrounding anidentified body image or body feature. Once an extractable body image isidentified, S230 functions to extract or crop an image of the body fromeach image frame and/or drop the non-body portions of the image frames.A new image file may be subsequently generated by S230, which includesonly the image of the body from each of the image frames.

Extracting the body image from each of the image frames functions toimprove the quality of input provided to subsequent image processingtechniques and prediction techniques of method 200. In particular, byextracting the body image, alone, and eliminating extraneous object andsurrounding information (i.e., non-body objects or non-bodycircumstances) reduces the computing power needed to track the bodywithin each image frame because there is less image data to evaluate bythe body tracking module and the like.

Additionally, once a body image is extracted from each of the imageframes, S230 functions to transmit the extracted body image from eachimage frame to a body tracking module, a facial detection module, and/ora body movement prediction module. S230, preferably, transmits theextracted body image for each image frame to the facial detection moduleand the body tracking module, in parallel, such that the facialdetection and/or facial recognition processing and the body trackingS260 are performed asynchronously and at a same time thereby enablingefficiency in tracking the body and identifying the user.

S240, which includes transmitting the cropped image of the body to abody tracking module and to a facial detection module, may function totransmit, in parallel, the extracted image of the body to the bodytracking module (from either the peripheral cameras or the secondmoveable camera) and to the facial recognition module for the purpose ofsimultaneous (parallel) processing. Thus, in some circumstances, evenwhen an identity of the user associated with the body is not readilyknown, the method 200 may function to track the body and likely providea generic (but potentially differentiating) identifier, such as user 1.Once a user's identification is determined via the facial recognitionmodule or some other biometric-based identification module, the methodmay update the user 1 value with the determined name or identity of theuser associated with the body.

S240 may function to transmit in a continuous manner image frames havinga detected body as the image frames are captured by one or more imagecapturing devices. Alternatively, S240 may function to transmit theimage frames once a threshold number of image frames are met oraccording to a predetermined transition schedule.

S250, which includes cropping an image of a head (face) of the body,functions to extract an image of the head (including a face) of theextracted body image from each image frame. That is, in someembodiments, once a body is detected in an image and an image of thebody is extracted from an image frame (S230) and using a head or facedetection algorithm, S250 analyzes the image of the extracted body todetect a location of a head (or face) of the body. Once a location ofthe head of the body is determined, S250 may function to localize anextracting process to extract only the portion of the extracted bodyimage that corresponds to the head of the body, as shown in FIG. 3.

Once an image of the head is extracted from each extracted body imageframe, S250 may function to transmit a separate image file including theimage of the head of the body to a facial recognition module (S270)specifically configured for identifying a user associated with the head.In some embodiments, the facial recognition module implements a facialrecognition algorithm to specifically identify a user (e.g., a user'sname or other identifier) using the images from the peripheral camera.Additionally, or alternatively, the facial recognition module maycompare the image of the head or features of the image of the head to adatabase that includes stored facial images of potential users and/orfacial image features (e.g., eyes, nose, ears, lips, chin, etc.) ofpotential users.

Accordingly, the facial recognition module may function to determine anidentity of a user based on the extracted image of the head based on theimages originally captured by the peripheral cameras. However, in somecases, the images from the peripheral camera may not be of sufficientimage quality or image resolution to allow the facial recognition moduleto accurately determine a user's identity from the extracted image ofthe head. That is, the images captured by the peripheral cameras mayhave insufficient pixels to match captured images to stored images ofusers according to a minimal match or facial recognition confidencelevel. In some embodiments, the minimal match or minimal facialrecognition confidence level is based on a minimal quality and/or numberof pixels in an extracted facial and/or an extracted body image. In thisregard, the facial recognition module may return an indicationindicating a facial recognition matching failure (e.g., insufficientpixels in image, etc.) or an indication of no facial match with theimage of the head. This indication or the like by the facial recognitionmodule may trigger the generation of instructions to trigger a secondcamera (image capturing device) of the system having a greater focallength than the peripheral cameras that is capable of capturing an imagewith greater image resolution (e.g., greater pixels) and thus, capableof providing sufficient image data of the scene where motion is detectedto perform at least the facial recognition of the user.

Accordingly, S255, which includes automatically triggering a secondmoveable camera to capture an image of a face of the body, functions toidentify a location of a head of the body in a scene and capture imagesof the head of the body. That is, instructions to the second moveablecamera may include granular instructions to capture images of the headof a body rather than the entire body within a scene. In suchembodiments, the second moveable camera may be capable of remotedirectional and zoom control.

As mentioned above, the initial extracted body image may also includecoordinate data that indicates a location of the body within the definedspace or within an image scene that may then be passed to the secondmoveable camera for generating positioning data for the second moveablecamera and locating the body in a scene. For instance, the coordinatedata may include general coordinates of the body within the space and/orspecific coordinates of a component of the body (e.g., coordinates ofthe head or face of the body). While, in some embodiments, when the bodyis moving infrequently, moving slowly, or becomes immobile, thecoordinate data obtained with the extracted body image may be helpful;however, it may be necessary in other embodiments in which the bodymoves frequently or changes positions within a scene rapidly thatpredicted positioning data for the body is provided to the secondmoveable camera. Thus, according to a calculated state of the body,either actual position data or predicted position data of the body maybe provided to the second camera. In such embodiments, a first state ofthe body may include a state in which the body does not move betweenimage frames or below or at a movement threshold and a second state ofthe body in which the body moves at or above a movement threshold.Calculating a state of the body preferably includes calculating a speedof movement of the body within the space based on movement of the bodywithin the captured images.

Accordingly, depending on a calculated movement mode (e.g.,slow-movement mode or fast-movement mode) of the body throughout theimage frames, the second moveable camera may be set to receive eitheractual body image coordinates or predicted body image coordinates. Forinstance, in the case of a calculated slow- movement mode, actual bodyimage coordinates may be provided to the second moveable camera to beused as input in generating positioning data. On the other hand, in thecircumstance involving a calculated fast-moving mode, predicted bodyimage coordinates may be provided to the second moveable camera to beused as input for determining positioning data.

S260, which includes initiating body tracking and augmentation of bodytracking data to a body image frame (or file), functions to receive, asinput, the extracted body images and implement a tracking of themovement of the body throughout each of the frames and further, generateassistive data (e.g., tracking data) that may be utilized in one or moreadditional techniques of method 200.

As mentioned above, the body tracking module functions to track an imageof the body throughout each of the images frames. The body trackingmodule includes functionality to append or associate with each of theimage frames including an image of the body, tracking data includingcoordinate data of the image of the body within the frame or withrespect to the defined space. The additional data generated by the bodytracking module may include temporal (e.g., time-based, timestamps,etc.) data and other contextual data regarding the circumstancessurrounding the body image.

Additionally, or alternatively, S260 may function to use the bodytracking module to generate identity inferences for tracking a bodywithin a space even when lapses in the tracking of the body occurs. Thatis, in some instances, a body that is being tracked may move aboutdisparate locations in space that may include blind spots in which theimage capturing devices are not able to capture images of the body, thebody moves between spaces in which image capturing devices are notpresent, or the body moves between spaces in which another systemimplementing the method 200 begins a body tracking of the body. In suchinstances, S230 may enable the body tracking module to infer that a bodythat is being tracked is a same body or user even when there is a lapsebased on a determination that the body did not enter or exit arecognized entry or exit of the space. That is, re-confirmation of auser identity may not be required when the body tracking module losestrack of the body for a period but is able to confirm that the body didnot enter a recognized entry of a space nor exit a recognized exit ofthe space.

Additionally, or alternatively, S260 may function to operate the bodytracking module to periodically and/or actively re-confirm an identityof a user associated with a body that is actively tracked. That is,after a predetermined number of image frames are captured that include abody being tracked, S260 may function to re-confirm an identity bytransmitting images of the body being tracked to one or more of therecognition modules to validate that a current user identity of the bodybeing tracked by the body tracking module is the same user. In this way,accuracy of the body tracking and associated user identity may befurther ensured even if a user travels between a number of regions of aspace or between spaces (that include a number of disparate systems) inwhich visibility of the user's motion is degraded or obstructed acrossone or more points in time during a continuous (or discontinuous)tracking of the user.

Additionally, or alternatively, S260 may include generating an ephemeralidentifier for each body identified and being tracked in an image frame,as shown in FIG. 4. For instance, if the body tracking module istracking two bodies, the body tracking module may associate with a firstbody the identifier, ID-1, and may associate with the second body theidentifier, ID-2. Thus, for each frame at time, t_(n) (where n>0, n isan integer), the body tracking module associated coordinate data and anidentifier for each body image in a frame. An example frame may bedescribed as follows: Frame 1: Body Image, ID-1 at [X1, Y1, Z1], Time1;Frame 2: Body Image, ID-1 at [X2, Y2, Z2], Time2 . . . If a second bodyimage was in a same scene and frame as a first body image, the secondbody image would be described as Body Image, ID-2 having its owncoordinate information.

Additionally, or alternatively, in some embodiments, the body trackingmodule is initialized with localization data to determine the positionof a system implementing method 200 relative to other features andobjects within a room. Thus, in operation, S260 may function to identifycoordinate information (i.e., localization data) of the body image withrespect to areas of the defined space, with respect to a location of thesystem implementing method 200, or with respect to a position of thebody image within an image frame. Any variation or combination of thisposition and/or tracking data may be associated with each image frame.

Additionally, the captured image data and/or the body tracking data maybe transmitted to a body movement prediction module for predictingpositioning data of a body image within one or more future image frames.

Optionally, S265, which includes predicting a movement of the bodywithin the space, preferably functions to implement a body movementprediction module to predict a location of the body image within thesame image scene or within another image scene. Accordingly, in someembodiments, S230 may additionally or alternatively provide the capturedimage data from a scene in the space to a body movement predictionmodule. S235, preferably, comprises a body movement prediction algorithm(or any suitable predictive algorithm including predictive machinelearning models) that analyzes one or more historical image frames topredict a trajectory of the body. For instance, the body movementprediction module may receive, as input (e.g., sample data) tenhistorical image frames having a body identified therein to predict thenext five image frames of a same scene and/or the next five image framesof another scene having an image of the body present within the frames.

S265 may also function to predict granular coordinate information foreach of the predicted frames. For instance, S265 may include predictinga future X, Y, and Z coordinate within the space for each of thepredicted image frames. Additionally, or alternatively, S235 maytransmit the predicted image frames together with the predictedcoordinate data to one or more additional processing of the method 200.As an example, the predicted image frames and the predicted coordinatedata may be transmitted to a PTZ camera or the like to be used as inputto generate advanced movement instructions (e.g., moving the camera tothe predicted locations) for performing one or more movements of the PTZcamera ahead of the detected body moving into a field-of-view of the PTZcamera. That is, the PTZ camera or image capturing device may functionto anticipate a future location of the body and position itsfield-of-view in the future location in advance of the body arriving tothe future location.

S270, which includes identifying an identity of a user using the face ofthe body, functions to implement a facial recognition module that uses,as input, an image of the head or face of the body captured from thesecond moveable camera to determine an identity of a user associatedwith the image of the face or image of the head.

Accordingly, S270 may pass the image of the head or face of the body asinput into a facial recognition module executing a facial recognitionalgorithm to determine the user's identity. Additionally, oralternatively, the facial recognition module may compare the image ofthe face or head or features thereof to an image/user ID database untila match is determined. Preferably, the image/user ID database is a localdatabase residing on a system implementing method 200.

Optionally, or additionally, S275, which includes implementing amulti-tiered validation of a calculated user identity, functions toenhance and/or validate the identification of a user associated with thebody.

In one implementation, S275 may function to use additional and/or anysuitable biometric data including voice data, joint data and/or gaitdata derived from the one or more images of the body and/or acousticdata to enhance user identification that may typically be performed atthe facial recognition module. In particular, S275 may function toimplement a gait recognition module and/or a body joint recognitionmodule that may be used to identify a user according to their gaitand/or body joints, respectively.

In some embodiments, the user identification results of one or both ofthe joint recognition module and the gait recognition module may be usedin lieu of the user identification results of the facial recognitionmodule.

In some embodiments, the joint data and the gait data may be used as afirst tier and a second tier for validating a user identificationdetermined according to the method 200. For instance, if the facialrecognition module provides a user identification having a lowconfidence, the results of the gait recognition module and/or the jointrecognition module may be used to validate or invalidate the useridentification. Accordingly, in some embodiments, use of a higherresolution image capturing device may not be required if gait dataand/or joint data are captured and used for validation.

Similarly, gait data and/or joint data may be used to improve a speedand/or efficiency in determining user identification (e.g., from afacial recognition module), which is typically based solely on resultsof a facial recognition module. For instance, the gait recognitionmodule and/or the joint recognition module may be implemented inparallel with the processing of the facial recognition module, suchthat, even with partial completion of the user identification processesof any of the gait, joint, or facial recognition modules, a highconfidence user identification may be provided for a given body. Forinstance, if the joint, gait, and facial recognition modules are run inparallel, a first of the modules may process the image data to 50%, asecond of the modules at 55%, and a third of the modules at 60% processcompletion. However, if the user identification at each of the modulesis consistent, even at partial completion of the user identificationprocess, S255 may output a user identification of a user associated witha body that matches the same or consistent user identification at eachof the modules. In this way, the user identification process of a bodymay be expedited even with partial process.

S280, which includes identifying an identity of a user using the face ofthe body S270 and updating the tracking data with the identity of theuser, functions to, in response to determining an identity of a userassociated with the head or face of the body, transmit to the bodytracking module the determined identity of the user so that the bodytracking module may update the body tracking frames with specific useridentification (ID) data (e.g., a user's name, a recognized useridentifier, or the like).

Once a user's ID is determined by the facial recognition module andprovided to the body tracking module, the facial recognition processingat either or both of S250 and S270 may be stopped or made idle, in someembodiments. Thus, the method 200 also ceases to transmit the extractedhead or face images to the facial recognition module; however, themethod 200 continues to extract body images and provide the same to thebody tracking module.

The body tracking module may additionally function to update itstracking information to positively include the user's identityassociated with the body image. Keeping with the prior example above,the frame data at the body tracking module may be represented, asfollows: Frame 1: Body Image, John Does at [X1, Y1, Z1], Time1; Frame 2:Body Image, John Doe at [X2, Y2, Z2], Time2.

Accordingly, once a specific user identity is recognized and associatedwith the body image tracked by the method 200, applications andfunctionality associated with the system may be implemented. Forinstance, prestored user preferences with respect to any number ofapplications accessible and/or stored on a system implementing themethod 200 that are associated with an identified user identity may beactivated.

Additionally, or alternatively, S280 may function to periodicallyre-validate or re-confirm an identity of a previously identified user.In some embodiments, S280 re-initiates facial extraction and facialrecognition of a previously identified user that is being tracked in aspace to ensure accuracy of an ongoing body tracking and useridentification.

Accordingly, S280 may function to re-validate or re-confirm an identityof a previously identified user based on one or more of a passage oftime (e.g., every 15 minutes, etc.), a detection of an event (e.g., anentry of a new body into the monitored space), an increased movement ofa user or increased movement of multiple users in a space, a movement ofa user between disparate spaces, upon an entry or exit of a tracked userin a monitored space, and the like. It shall be noted that re-validationand/or re-confirmation of identity may be based on any suitable event orpredetermined schedule.

The system and methods of the preferred embodiment and variationsthereof can be embodied and/or implemented at least in part as a machineconfigured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are preferably executedby computer-executable components preferably integrated with the systemand one or more portions of the processor and/or the controller. Thecomputer-readable medium can be stored on any suitable computer-readablemedia such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD orDVD), hard drives, floppy drives, or any suitable device. Thecomputer-executable component is preferably a general or applicationspecific processor, but any suitable dedicated hardware orhardware/firmware combination device can alternatively or additionallyexecute the instructions.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

What is claimed is:
 1. An intelligent user detection, localization,and/or identification system, the system comprising: a plurality ofperipheral image capturing devices arranged about a body of the system,wherein the plurality of peripheral image capturing device capture oneor more images of a scene within a space; a database storing biometricimage data of one or more users; a body extraction module that extractsat least one image of the body from the one or more images of the scene,wherein the body extraction module further extracts an image of a headfrom the extracted at least one image of the body; a body trackingmodule that tracks the body within the space based on the extracted atleast one image of the body; a facial recognition module that performsfacial recognition of the extracted image of the head of the body inparallel with the body tracking module; a processing circuit that:determines an identity of a user associated with the body based onresults of the facial recognition module; in response to determining theidentity of the user, augments the image frames of the captured one ormore images that include the body with identity data based on thedetermined identity of the user.
 2. The system according to claim 1,further comprising: one or more movement detection sensors that detectmotion and trigger an operation of the plurality of peripheral imagecapturing devices.
 3. The system according to claim 1, furthercomprising: a high-resolution camera that captures images of the sceneat a higher image resolution than the plurality of peripheral cameras,wherein the high-resolution camera is triggered to operate when an imagequality of the one or more images captured by the plurality ofperipheral cameras is below a facial recognition threshold.
 4. Thesystem according to claim 1, wherein tracking the body includes:augmenting each of the image frames of the captured one or more imagesthat include the body with tracking data, wherein the tracking datacomprises coordinate data of a position of the body within the space;and predicting a movement of the body within the space based on theimage frames having tracking data.
 5. A method for automaticallytracking a user within a space and determining an identity of the userbased on image data, the method comprising: automatically detecting, bya motion sensor, motion in a space; in response to detecting the motionin the space, capturing one or more images of a scene in a direction ofthe motion within the space by one or more of a plurality of peripheralimage capturing devices and detecting a body of a person within at leastone of the one or more images of the scene; extracting at least oneimage of the body from the one or more images of the scene; identifyinga head of the body based on the extracted at least one image of the bodyand extracting an image of a head from the extracted at least one imageof the body; transmitting the extracted at least one image of the bodyto a body tracking module; transmitting the extracted image of the headto a face detection module; in parallel, (i) tracking the body withinthe space based on the extracted at least one image of the body and (ii)performing by a facial recognition module facial recognition of theextracted image of the head of the body based on the extracted image ofthe head; determining an identity of a user associated with the bodybased on results of the facial recognition module; and in response todetermining the identity of the user, augmenting image frames of thecaptured one or more images that include the body within the scene withidentity data based on the determined identity of the user.
 6. Themethod of claim 5, further comprising: identifying an image resolutionof the extracted image of the head of the body; determining that theimage resolution of the extracted image of the head of the body does notsatisfy a minimal facial recognition threshold; in response todetermining that the image resolution does not satisfy the minimalfacial recognition threshold, providing an activation signal to ahigh-resolution image capturing device distinct from the plurality ofperipheral cameras; and capturing by the high-resolution image capturingdevice one or more high-resolution images of a scene within the space.7. The method of claim 6, wherein: the minimal facial recognitionthreshold comprises a minimal image resolution required to successfullyperform facial recognition of a user based on image data of a head, andthe high-resolution image capturing device comprises an image capturingdevice that captures images that satisfy the minimal facial recognitionthreshold.
 8. The method of claim 6, further comprising: identifying thebody of the person within the one or more high-resolution images of thescene; extracting at least one high-resolution image of the body fromthe one or more high-resolution images; extracting a high-resolutionimage of a head of the body from the at least one high-resolution imageof the body; transmitting to the facial recognition module the extractedhigh-resolution image of the head of the body, wherein determining theidentity of the user associated with the body is based on results of thefacial recognition using the extracted high-resolution image of the headof the body.
 9. The method of claim 5, further comprising: augmentingeach of the image frames of the captured one or more images that includethe body with tracking data, wherein the tracking data comprisescoordinate data of a position of the body within the space; andpredicting a movement of the body within the space based on the imageframes having tracking data.
 10. The method according to claim 9,wherein the prediction of the movement of the body comprises a predictedtrajectory that identifies a plurality of future coordinate locations ofthe body within the space.
 11. The method according to claim 10, furthercomprising: selectively activating a subset of the plurality ofperipheral image capturing devices having a field-of-view in one or moreareas within the space based on the predicted trajectory of the body.12. The method according to claim 5, further comprising: augmentingtracking data, by the body tracking module, to each of the image framesthat includes an image of the body, wherein the tracking data comprisescoordinate data comprising X-axis coordinate data and Y-axis coordinatedata of the body based on an X-Y coordinate system defined for thespace, wherein the X-Y coordinate system is based on a position of theplurality of the peripheral image capturing devices functioning as apoint of origin of the X-Y coordinate system in the space.
 13. Themethod according to claim 5, further comprising: providing the one ormore captured images as input into an object detection module; using theobject detection module to detect one or more objects having an image ofa body and/or an image of a face included in the one or more objects;flagging the one or more detected objects as objects that include datacapable of triggering a false positive identification of a body or auser; and providing data relating to the one or more detected objects asinput into a body detection module.
 14. The method of claim 6, wherein:the high-resolution image capturing device comprises a movable imagecapturing device that is movable relative to stationary positions of theplurality of peripheral image capturing devices, and the high-resolutionimage capturing device includes a greater focal length than a focallength of each of the plurality of peripheral cameras.
 15. The method ofclaim 6, further comprising: calculating a speed of movement of a bodyin a plurality of images of the body captured by the plurality ofperipheral cameras, wherein if the speed of movement of the body doesnot satisfy a movement threshold, providing actual coordinate data ofthe body as input for generating movement instructions for thehigh-resolution image capturing device; and generating movementinstructions for the high-resolution image capturing device based on theactual coordinate data of the body.
 16. The method according to claim 6,further comprising: calculating a speed of movement of a body in aplurality of images of the body captured by the plurality of peripheralcameras, wherein if the speed of movement of the body satisfies amovement threshold, providing predicted coordinate data of the body asinput for generating movement instructions for the high-resolution imagecapturing device; and generating movement instructions for thehigh-resolution image capturing device based on the actual coordinatedata of the body.
 17. The method according to claim 5, furthercomprising: augmenting the one or more images with an ephemeralidentifier for the body recognized in the one or more images; and inresponse to determining the identity of the user associated with thebody, replacing the ephemeral identifier with the identity of the user.18. The method according to claim 5, further comprising: providing theone or more images of the scene as input into a gait recognition module;using the gait recognition module to determine the identity of the userbased on gait data derived from the one or more images; and using thegait data to validate the identity of the user generated by the facialrecognition module.
 19. The method according to claim 5, furthercomprising: providing the one or more images of the scene as input intoa body joint recognition module; using the body joint recognition moduleto determine the identity of the user based on body joint data derivedfrom the one or more images; and using the body joint data to validatethe identity of the user generated by the facial recognition module. 20.A method comprising: automatically detecting, by a presence sensor, apresence of a human body in a space; in response to detecting thepresence of the human body in the space, capturing one or more images ofa scene in a direction of the presence within the space by one or moreof a plurality of peripheral image capturing devices and detecting abody of a person within at least one of the one or more images of thescene; extracting at least one image of the body from the one or moreimages of the scene; identifying a head of the body based on theextracted at least one image of the body and extracting an image of ahead from the extracted at least one image of the body; transmitting afirst image file comprising the extracted at least one image of the bodyto a body tracking module; transmitting a second image file comprisingthe extracted image of the head to a face detection module; in parallel,(i) tracking the body within the space based on the extracted at leastone image of the body and (ii) performing by a biometric recognitionmodule biometric recognition of the extracted image of the head of thebody based on the extracted image of the head; determining an identityof a user associated with the body based on results of the biometricrecognition module; and in response, augmenting image frames of thecaptured one or more images that include the body within the scene withidentity data based on the determined identity of the user.